Example-Based Metonymy Recognition for Proper NounsYves PeirsmanQuantitative Lexicology and Variational LinguisticsUniversity of Leuven,Belgiumyves.peirsman@arts.kuleuven.beAbstractMetonymy recognition is generally ap-proached with complex algorithms thatrely heavily on the manual annotation oftraining and test data.This paper will re-lieve this complexity in two ways.First,it will show that the results of the cur-rent learning algorithms can be replicatedby the‘lazy’algorithm of Memory-BasedLearning.This approach simply stores alltraining instances to its memory and clas-sifies a test instance by comparing it to alltraining examples.Second,this paper willargue that the number of labelled trainingexamples that is currently used in the lit-erature can be reduced drastically.Thisfinding can help relieve the knowledge ac-quisition bottleneck in metonymy recog-nition,and allow the algorithms to be ap-plied on a wider scale.1IntroductionMetonymy is afigure of speech that uses“one en-tity to refer to another that is related to it”(Lakoff and Johnson,1980,p.35).In example(1),for in-stance,China and Taiwan stand for the govern-ments of the respective countries:(1)China has always threatened to use forceif Taiwan declared independence.(BNC) Metonymy resolution is the task of automatically recognizing these words and determining their ref-erent.It is therefore generally split up into two phases:metonymy recognition and metonymy in-terpretation(Fass,1997).The earliest approaches to metonymy recogni-tion identify a word as metonymical when it vio-lates selectional restrictions(Pustejovsky,1995).Indeed,in example(1),China and Taiwan both violate the restriction that threaten and declare require an animate subject,and thus have to be interpreted metonymically.However,it is clear that many metonymies escape this characteriza-tion.Nixon in example(2)does not violate the se-lectional restrictions of the verb to bomb,and yet, it metonymically refers to the army under Nixon’s command.(2)Nixon bombed Hanoi.This example shows that metonymy recognition should not be based on rigid rules,but rather on statistical information about the semantic and grammatical context in which the target word oc-curs.This statistical dependency between the read-ing of a word and its grammatical and seman-tic context was investigated by Markert and Nis-sim(2002a)and Nissim and Markert(2003; 2005).The key to their approach was the in-sight that metonymy recognition is basically a sub-problem of Word Sense Disambiguation(WSD). Possibly metonymical words are polysemous,and they generally belong to one of a number of pre-defined metonymical categories.Hence,like WSD, metonymy recognition boils down to the auto-matic assignment of a sense label to a polysemous word.This insight thus implied that all machine learning approaches to WSD can also be applied to metonymy recognition.There are,however,two differences between metonymy recognition and WSD.First,theo-retically speaking,the set of possible readings of a metonymical word is open-ended(Nunberg, 1978).In practice,however,metonymies tend to stick to a small number of patterns,and their la-bels can thus be defined a priori.Second,classic 71WSD algorithms take training instances of one par-ticular word as their input and then disambiguate test instances of the same word.By contrast,since all words of the same semantic class may undergo the same metonymical shifts,metonymy recogni-tion systems can be built for an entire semantic class instead of one particular word(Markert and Nissim,2002a).To this goal,Markert and Nissim extracted from the BNC a corpus of possibly metonymical words from two categories:country names (Markert and Nissim,2002b)and organization names(Nissim and Markert,2005).All these words were annotated with a semantic label —either literal or the metonymical cate-gory they belonged to.For the country names, Markert and Nissim distinguished between place-for-people,place-for-event and place-for-product.For the organi-zation names,the most frequent metonymies are organization-for-members and organization-for-product.In addition, Markert and Nissim used a label mixed for examples that had two readings,and othermet for examples that did not belong to any of the pre-defined metonymical patterns.For both categories,the results were promis-ing.The best algorithms returned an accuracy of 87%for the countries and of76%for the orga-nizations.Grammatical features,which gave the function of a possibly metonymical word and its head,proved indispensable for the accurate recog-nition of metonymies,but led to extremely low recall values,due to data sparseness.Therefore Nissim and Markert(2003)developed an algo-rithm that also relied on semantic information,and tested it on the mixed country data.This algo-rithm used Dekang Lin’s(1998)thesaurus of se-mantically similar words in order to search the training data for instances whose head was sim-ilar,and not just identical,to the test instances. Nissim and Markert(2003)showed that a combi-nation of semantic and grammatical information gave the most promising results(87%). However,Nissim and Markert’s(2003)ap-proach has two major disadvantages.Thefirst of these is its complexity:the best-performing al-gorithm requires smoothing,backing-off to gram-matical roles,iterative searches through clusters of semantically similar words,etc.In section2,I will therefore investigate if a metonymy recognition al-gorithm needs to be that computationally demand-ing.In particular,I will try and replicate Nissim and Markert’s results with the‘lazy’algorithm of Memory-Based Learning.The second disadvantage of Nissim and Mark-ert’s(2003)algorithms is their supervised nature. Because they rely so heavily on the manual an-notation of training and test data,an extension of the classifiers to more metonymical patterns is ex-tremely problematic.Yet,such an extension is es-sential for many tasks throughout thefield of Nat-ural Language Processing,particularly Machine Translation.This knowledge acquisition bottle-neck is a well-known problem in NLP,and many approaches have been developed to address it.One of these is active learning,or sample selection,a strategy that makes it possible to selectively an-notate those examples that are most helpful to the classifier.It has previously been applied to NLP tasks such as parsing(Hwa,2002;Osborne and Baldridge,2004)and Word Sense Disambiguation (Fujii et al.,1998).In section3,I will introduce active learning into thefield of metonymy recog-nition.2Example-based metonymy recognition As I have argued,Nissim and Markert’s(2003) approach to metonymy recognition is quite com-plex.I therefore wanted to see if this complexity can be dispensed with,and if it can be replaced with the much more simple algorithm of Memory-Based Learning.The advantages of Memory-Based Learning(MBL),which is implemented in the T i MBL classifier(Daelemans et al.,2004)1,are twofold.First,it is based on a plausible psycho-logical hypothesis of human learning.It holds that people interpret new examples of a phenom-enon by comparing them to“stored representa-tions of earlier experiences”(Daelemans et al., 2004,p.19).This contrasts to many other classi-fication algorithms,such as Naive Bayes,whose psychological validity is an object of heavy de-bate.Second,as a result of this learning hypothe-sis,an MBL classifier such as T i MBL eschews the formulation of complex rules or the computation of probabilities during its training phase.Instead it stores all training vectors to its memory,together with their labels.In the test phase,it computes the distance between the test vector and all these train-ing vectors,and simply returns the most frequentlabel of the most similar training examples.One of the most important challenges inMemory-Based Learning is adapting the algorithmto one’s data.This includesfinding a represen-tative seed set as well as determining the rightdistance measures.For my purposes,however, T i MBL’s default settings proved more than satis-factory.T i MBL implements the IB1and IB2algo-rithms that were presented in Aha et al.(1991),butadds a broad choice of distance measures.Its de-fault implementation of the IB1algorithm,whichis called IB1-IG in full(Daelemans and Van denBosch,1992),proved most successful in my ex-periments.It computes the distance between twovectors X and Y by adding up the weighted dis-tancesδbetween their corresponding feature val-ues x i and y i:∆(X,Y)=ni=1w iδ(x i,y i)(3)The most important element in this equation is theweight that is given to each feature.In IB1-IG,features are weighted by their Gain Ratio(equa-tion4),the division of the feature’s InformationGain by its split rmation Gain,the nu-merator in equation(4),“measures how much in-formation it[feature i]contributes to our knowl-edge of the correct class label[...]by comput-ing the difference in uncertainty(i.e.entropy)be-tween the situations without and with knowledgeof the value of that feature”(Daelemans et al.,2004,p.20).In order not“to overestimate the rel-evance of features with large numbers of values”(Daelemans et al.,2004,p.21),this InformationGain is then divided by the split info,the entropyof the feature values(equation5).In the followingequations,C is the set of class labels,H(C)is theentropy of that set,and V i is the set of values forfeature i.w i=H(C)− v∈V i P(v)×H(C|v)2This data is publicly available and can be downloadedfrom /mnissim/mascara.73P F86.6%49.5%N&M81.4%62.7%Table1:Results for the mixed country data.T i MBL:my T i MBL resultsN&M:Nissim and Markert’s(2003)results simple learning phase,T i MBL is able to replicate the results from Nissim and Markert(2003;2005). As table1shows,accuracy for the mixed coun-try data is almost identical to Nissim and Mark-ert’sfigure,and precision,recall and F-score for the metonymical class lie only slightly lower.3 T i MBL’s results for the Hungary data were simi-lar,and equally comparable to Markert and Nis-sim’s(Katja Markert,personal communication). Note,moreover,that these results were reached with grammatical information only,whereas Nis-sim and Markert’s(2003)algorithm relied on se-mantics as well.Next,table2indicates that T i MBL’s accuracy for the mixed organization data lies about1.5%be-low Nissim and Markert’s(2005)figure.This re-sult should be treated with caution,however.First, Nissim and Markert’s available organization data had not yet been annotated for grammatical fea-tures,and my annotation may slightly differ from theirs.Second,Nissim and Markert used several feature vectors for instances with more than one grammatical role andfiltered all mixed instances from the training set.A test instance was treated as mixed only when its several feature vectors were classified differently.My experiments,in contrast, were similar to those for the location data,in that each instance corresponded to one vector.Hence, the slightly lower performance of T i MBL is prob-ably due to differences between the two experi-ments.Thesefirst experiments thus demonstrate that Memory-Based Learning can give state-of-the-art performance in metonymy recognition.In this re-spect,it is important to stress that the results for the country data were reached without any se-mantic information,whereas Nissim and Mark-ert’s(2003)algorithm used Dekang Lin’s(1998) clusters of semantically similar words in order to deal with data sparseness.This fact,togetherAcc RT i MBL78.65%65.10%76.0%—Figure1:Accuracy learning curves for the mixed country data with and without semantic informa-tion.in more detail.4Asfigure1indicates,with re-spect to overall accuracy,semantic features have a negative influence:the learning curve with both features climbs much more slowly than that with only grammatical features.Hence,contrary to my expectations,grammatical features seem to allow a better generalization from a limited number of training instances.With respect to the F-score on the metonymical category infigure2,the differ-ences are much less outspoken.Both features give similar learning curves,but semantic features lead to a higherfinal F-score.In particular,the use of semantic features results in a lower precisionfig-ure,but a higher recall score.Semantic features thus cause the classifier to slightly overgeneralize from the metonymic training examples.There are two possible reasons for this inabil-ity of semantic information to improve the clas-sifier’s performance.First,WordNet’s synsets do not always map well to one of our semantic la-bels:many are rather broad and allow for several readings of the target word,while others are too specific to make generalization possible.Second, there is the predominance of prepositional phrases in our data.With their closed set of heads,the number of examples that benefits from semantic information about its head is actually rather small. Nevertheless,myfirst round of experiments has indicated that Memory-Based Learning is a sim-ple but robust approach to metonymy recogni-tion.It is able to replace current approaches that need smoothing or iterative searches through a the-saurus,with a simple,distance-based algorithm.Figure3:Accuracy learning curves for the coun-try data with random and maximum-distance se-lection of training examples.over all possible labels.The algorithm then picks those instances with the lowest confidence,since these will contain valuable information about the training set(and hopefully also the test set)that is still unknown to the system.One problem with Memory-Based Learning al-gorithms is that they do not directly output prob-abilities.Since they are example-based,they can only give the distances between the unlabelled in-stance and all labelled training instances.Never-theless,these distances can be used as a measure of certainty,too:we can assume that the system is most certain about the classification of test in-stances that lie very close to one or more of its training instances,and less certain about those that are further away.Therefore the selection function that minimizes the probability of the most likely label can intuitively be replaced by one that max-imizes the distance from the labelled training in-stances.However,figure3shows that for the mixed country instances,this function is not an option. Both learning curves give the results of an algo-rithm that starts withfifty random instances,and then iteratively adds ten new training instances to this initial seed set.The algorithm behind the solid curve chooses these instances randomly,whereas the one behind the dotted line selects those that are most distant from the labelled training exam-ples.In thefirst half of the learning process,both functions are equally successful;in the second the distance-based function performs better,but only slightly so.There are two reasons for this bad initial per-formance of the active learning function.First,it is not able to distinguish between informativeandFigure4:Accuracy learning curves for the coun-try data with random and maximum/minimum-distance selection of training examples. unusual training instances.This is because a large distance from the seed set simply means that the particular instance’s feature values are relatively unknown.This does not necessarily imply that the instance is informative to the classifier,how-ever.After all,it may be so unusual and so badly representative of the training(and test)set that the algorithm had better exclude it—something that is impossible on the basis of distances only.This bias towards outliers is a well-known disadvantage of many simple active learning algorithms.A sec-ond type of bias is due to the fact that the data has been annotated with a few features only.More par-ticularly,the present algorithm will keep adding instances whose head is not yet represented in the training set.This entails that it will put off adding instances whose function is pp,simply because other functions(subj,gen,...)have a wider variety in heads.Again,the result is a labelled set that is not very representative of the entire training set.There are,however,a few easy ways to increase the number of prototypical examples in the train-ing set.In a second run of experiments,I used an active learning function that added not only those instances that were most distant from the labelled training set,but also those that were closest to it. After a few test runs,I decided to add six distant and four close instances on each iteration.Figure4 shows that such a function is indeed fairly success-ful.Because it builds a labelled training set that is more representative of the test set,this algorithm clearly reduces the number of annotated instances that is needed to reach a given performance.Despite its success,this function is obviously not yet a sophisticated way of selecting good train-76Figure5:Accuracy learning curves for the organi-zation data with random and distance-based(AL) selection of training examples with a random seed set.ing examples.The selection of the initial seed set in particular can be improved upon:ideally,this seed set should take into account the overall dis-tribution of the training examples.Currently,the seeds are chosen randomly.Thisflaw in the al-gorithm becomes clear if it is applied to another data set:figure5shows that it does not outper-form random selection on the organization data, for instance.As I suggested,the selection of prototypical or representative instances as seeds can be used to make the present algorithm more robust.Again,it is possible to use distance measures to do this:be-fore the selection of seed instances,the algorithm can calculate for each unlabelled instance its dis-tance from each of the other unlabelled instances. In this way,it can build a prototypical seed set by selecting those instances with the smallest dis-tance on average.Figure6indicates that such an algorithm indeed outperforms random sample se-lection on the mixed organization data.For the calculation of the initial distances,each feature re-ceived the same weight.The algorithm then se-lected50random samples from the‘most proto-typical’half of the training set.5The other settings were the same as above.With the present small number of features,how-ever,such a prototypical seed set is not yet always as advantageous as it could be.A few experiments indicated that it did not lead to better performance on the mixed country data,for instance.However, as soon as a wider variety of features is taken into account(as with the organization data),the advan-pling can help choose those instances that are most helpful to the classifier.A few distance-based al-gorithms were able to drastically reduce the num-ber of training instances that is needed for a given accuracy,both for the country and the organization names.If current metonymy recognition algorithms are to be used in a system that can recognize all pos-sible metonymical patterns across a broad variety of semantic classes,it is crucial that the required number of labelled training examples be reduced. This paper has taken thefirst steps along this path and has set out some interesting questions for fu-ture research.This research should include the investigation of new features that can make clas-sifiers more robust and allow us to measure their confidence more reliably.This confidence mea-surement can then also be used in semi-supervised learning algorithms,for instance,where the clas-sifier itself labels the majority of training exam-ples.Only with techniques such as selective sam-pling and semi-supervised learning can the knowl-edge acquisition bottleneck in metonymy recogni-tion be addressed.AcknowledgementsI would like to thank Mirella Lapata,Dirk Geer-aerts and Dirk Speelman for their feedback on this project.I am also very grateful to Katja Markert and Malvina Nissim for their helpful information about their research.ReferencesD.W.Aha, D.Kibler,and M.K.Albert.1991.Instance-based learning algorithms.Machine Learning,6:37–66.W.Daelemans and A.Van den Bosch.1992.Generali-sation performance of backpropagation learning on a syllabification task.In M.F.J.Drossaers and A.Ni-jholt,editors,Proceedings of TWLT3:Connection-ism and Natural Language Processing,pages27–37, Enschede,The Netherlands.W.Daelemans,J.Zavrel,K.Van der Sloot,andA.Van den Bosch.2004.TiMBL:Tilburg Memory-Based Learner.Technical report,Induction of Linguistic Knowledge,Computational Linguistics, Tilburg University.D.Fass.1997.Processing Metaphor and Metonymy.Stanford,CA:Ablex.A.Fujii,K.Inui,T.Tokunaga,and H.Tanaka.1998.Selective sampling for example-based wordsense putational Linguistics, 24(4):573–597.R.Hwa.2002.Sample selection for statistical parsing.Computational Linguistics,30(3):253–276.koff and M.Johnson.1980.Metaphors We LiveBy.London:The University of Chicago Press.D.Lin.1998.An information-theoretic definition ofsimilarity.In Proceedings of the International Con-ference on Machine Learning,Madison,USA.K.Markert and M.Nissim.2002a.Metonymy res-olution as a classification task.In Proceedings of the Conference on Empirical Methods in Natural Language Processing(EMNLP2002),Philadelphia, USA.K.Markert and M.Nissim.2002b.Towards a cor-pus annotated for metonymies:the case of location names.In Proceedings of the Third International Conference on Language Resources and Evaluation (LREC2002),Las Palmas,Spain.M.Nissim and K.Markert.2003.Syntactic features and word similarity for supervised metonymy res-olution.In Proceedings of the41st Annual Meet-ing of the Association for Computational Linguistics (ACL-03),Sapporo,Japan.M.Nissim and K.Markert.2005.Learning to buy a Renault and talk to BMW:A supervised approach to conventional metonymy.In H.Bunt,editor,Pro-ceedings of the6th International Workshop on Com-putational Semantics,Tilburg,The Netherlands. 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Chapter 1 Introduction1. What is linguistics? Linguistics is generally defined as the scientific study of language.2.The scope of linguistics:(1 ). phonetics 语音学；phonology 音位学；morphology 形态学；syntax 句法学；pragmatics 语用学(2). sociolinguistics 社会语言学；psycholinguistics 心理语言学；applied linguistics应用语言学3.Some important distinction in linguistics(1)Descriptive vs. prescriptive 描述性与规定性①If a linguistics study aims to describe and analyze the language people actually use,it is said to be descriptive;②If the linguistics study aims to lay down rules for” correct and standard” behaviorin using language, . to tell people what they should say and what they should notsay, it is said to be prescriptive.(2)Synchronic vs. diachronic 共时性与历时性①A synchronic description takes a fixed instant (usually, but not necessarily, thepresent) as its point of observation. Most grammars are of this kind.②Diachronic linguistics is the study of a language through the course of its history.(2)Langue & parole 语言与会话①Language refers to the abstract linguistic system shared by all the members of aspeech community.②Parole refers to the realization of language in actual use.(4)Competence and performance 语言能力与语言运用①A language user's unconscious knowledge about the system of rules is called hislinguistic competence.②Performance refers to the actual use of language in concrete situations.(5)speech and writing 语言与文字Speech and writing are the two major media of communication.(6)traditional grammar and modern linguistic 传统语法与现代语言学4.Definition of language:Language is a system of arbitrary vocal symbols used for human communication.Language is a system, ., elements of language are combined according to rules.Language is arbitrary in the sense that there is no intrinsic connection between a linguistics symbol and what the symbol stands.Language is vocal because the primary medium for all languages is sound.The term “human” in the definition is meant to specify that language is human-specific.5.Design features of language6.(1) Arbitrariness 任意性refers to the forms of linguistic signs bear no naturalrelationship to their meaning. (sounds and meanings)(2) Productivity(creativity)能产性Language is productive in that it makes possible theconstruction and interpretation of new signals by its users.(3) duality双重性The property of having two levels of structures, such that units of theprimary level are composed of elements of the secondary level and each of the two levels has its own principles of organization.(4) displacement移位性Human Languages enable their users to symbolize objects, eventsand concepts which are not present (in time and space) at moment of communication.(5)cultural transmission 文化传承性7.Functions of language(1) referential (to convey message and information),(2) poetic (to indulge in language for its own sake),(3) emotive (to express attitudes, feelings and emotions),(4) conative (to persuade and influence others through commands and requests),(5) phatic (to establish communion with others)(6) metalingual (to clear up intentions and meanings).①Informative(信息功能): to give information about facts. (ideational)②Interpersonal(人际功能): to establish and maintain social status in a society.(age,sex, language, background, accent, status)③Performative(施为功能) : language is used to do things, to perform certain actions.(name, promise, apologize, sorry, declare)④. Emotive/Expressive (情感功能): to express feelings and attitudes of the speaker.⑤Phatic communion(寒暄交流) : to use small and meaningless expressions to establisha comfortable relationship or maintain social contact between people without any factualcontent. (health, weather)⑥Recreational function(娱乐): the use of language for sheer joy. (lyrics, poetry)⑦Metalingual function(元语言功能): to talk about language itself.8.9.Chapter 2 Phonology1.Phonetics（语音学）is the study of the phonic medium of language; it is concerned with allthe sounds that occur in the world’s languages.Phonetics studies how speech sounds are produced, transmitted, and perceived.2.Orthographic representation of speech sounds：broad transcription（宽式标音）and narrow transcription（严式标音）A broad transcription（宽式标音）is the transcription with letter-symbols only.A narrow transcription（严式标音）is a transcription with letter symbols together withdiacritics.3.Phonology（音位学）is the study of the sound patterns and sound systems of languages.4.The differences between phonetics and phonology:（语音的正字表征）①Both are concerned with the same aspect of language----the speech sounds. But theydiffer in their approach and focus.②Phonetics is of general nature; it is interested in all the speech sounds used in all humanlanguages; it aims to answer questions like: how they are produced, how they differ fromeach other, what phonetic features they have, how they can be classified, etc.③Phonology aims to discover how speech sounds in a language form patterns and how these sounds are used to convey meaning in linguistic communication.5.Phone（音素）, phoneme（音位）, allophone（音位变体）A phone---- a phonetic unit or segment. The speech sounds we hear and produce duringlinguistic communication are all phones.A phoneme---- is a phonological unit; it is a unit of distinctive value; an abstract unit, not aparticular sound, but it is represented by a certain phone in certain phonetic context6.Phonemic contrast, complementary distribution and minimal pair.（音位对立，互补分布，最小对立体）7.Some rules of phonology（音位学规则）Sequential rules 序列规则Assimilation rule 同化规则Deletion rule省略规则8.Suprasegmental features （超音段特征）：stress重音，tone音调，intonation语调9.10.Chapter 3 Morphology1.Classification of words（1）Variable vs. invariable words:可变词类和不可变词类Variable words: One could find ordered and regular series of grammatically different word forms; on the other hand, part of the word remains constant follow, follows, following, followed; mat, matsInvariable words: those words such as since, when, seldom, through, hello. They do not have inflective endings.（2）Grammatical words vs. lexical words:语法词类和词汇词类Grammatical words: express grammatical meanings, such as conjunctions, prepositions, articles, pronounsLexical words: have lexical meanings, those which refer to substance, action and quality, such as nouns, verbs, adjectives, and adverbs.（3）Closed-class words vs. open-class words:封闭词类和开放词类Closed-class: a word whose membership is fixed or limited. New members are not regularly added. Pronouns, prepositions, conjunctions, articles, etc.Open-class: A word whose membership is in principle infinite or unlimited. Nouns, verbs, adjectives, adverbsGrammatical---lexical words closed-class---open-class words2.Morphere(词素)：the minimal meaningful unit of language.3.Linguistics use the term morphology to refer the part of the grammar that is concerned withword formation and word structure.4.Free morpheme & bound morpheme(自由语素和黏着语素)A morpheme which can be a word by itself is called a free morpheme; a morpheme thatmust be attached to another one is a bound morpheme.5.The variant forms of a morpheme are called its allomorphs.(词素变体)6.Inflectional affix & derivational affix(屈折词缀和派生词缀)pound: those words that consist of more than one free morphemes, the way to join twoseparate words to produce a single form.In compounds, the lexical morphemes can be of different word classes.pounds can be further divided into two kinds:the endocentric compound (向心复合词) the exocentric compound(离心复合词)9.Endocentric: one element serves as the head, the relationship of “a kind of”; eg self-control:a kind of control armchair: a kind of chair10.Exocentric: there is no head, so not a relationship of “a kind of something”, egscarecrow: not a kind of crow breakneck: not a kind of neck11.Chapter 4 Syntax1.What is Syntax （句法）?Syntax is the study of the rules governing the ways different constituents are combined to form sentences. 句法就是研究语言的不同成分组成句子的规则2.Syntactic relations can be analyzed into three kinds:relations of position 位置关系relations of substitutability 替代关系relations of co-occurrence 同现关系3.4.5.Chapter 5 Semantics1.What is Semantics?Semantics is the study of the meaning of words, phrases and sentences.语义学是研究单词、短语和句子的意义的学科2.The conceptualist view①The conceptualist view holds that there is no direct link between a linguistic form andwhat it refers to . between language and the real world); rather, in the interpretation of meaning they are linked through the mediation of concepts in the mind.②This is illustrated by the classic semantic triangle or triangle of significance suggested byOgden and Richard.Thought/reference/conceptSymbol/form referencentword/phrase/sentence③The symbol or form refers to the linguistic elements (words and phrases);Thereferent refers to the object in the world of experience;Thought or reference refers to concept.The symbol or a word signifies things by virtue of the concept associated with theform of the word in the minds of the speaker; and the concept looked at from thispoint of view is the meaning of the word.3.The contextualismMeaning should be studied in terms of situation, use, context—elements closely linked with language behavior. Two types of contexts are recognized:Situational context: spatiotemporal situationLinguistic context: the probability of a word’s co-occurrence or collocation.4.BehaviorismBehaviorists attempted to define meaning as “the situation in which the speaker utters it and the response it calls forth in the hearer”.5.Lexical meaningSense and reference are both concerned with the study of word meaning. They are two related but different aspects of meaning.Sense---- is concerned with the inherent meaning of the linguistic form. It is the collection of all the features of the linguistic form; it is abstract and de-contextualized. It is the aspect of meaning dictionary compilers are interested in.Reference----what a linguistic form refers to in the real, physical world; it deals with the relationship between the linguistic element and the non-linguistic world of experience.6.Major sense relations（1）Synonymy 同义词①Dialect synonymy 方言同义词②Stylistic synonymy 文体同义词③Synonyms that differ in their emotive or evaluative meaning④Collocational synonyms⑤Semantically different synonyms（2）Antonym 反义词①Gradable antonyms 等级反义词②Complementary antonyms 互补反义词③Relational opposites 关系反义词（3）Polysemy 一词多义（4）Homonymy 同形异义词（5）Hyponymy 上下义关系①Superordinate 上义词②Hyponyms下义词ponential analysis 成分分析法——a way of analyze lexical meaningIt is a way proposed by the structural semanticists to analyze word meaning.The approach is based on the belief that the meaning of a word can be dissected into meaning components, called semantic features.8.。
